The <i>cofradías</i>’ role within the Fisheries Local Action Groups system: Implications for small‐scale fisheries in Galicia (Spain)
Bibliographic record
Abstract
Abstract Structural funds for fisheries have a long history in the European Union, but the use of public funds for the sustainable development of fisheries‐dependent areas is a relatively recent practice. This approach is based on the concept of sustainable fisheries management and focuses on increasing the importance of the involvement of local communities in conservation and management. Fisheries Local Action Groups (FLAGs), created to harmonise social development in a broad sense with the specific, sectoral development of fishing, have become key players in this process in some European countries. In Galicia (NW Spain), FLAGs started to be created in 2008, and soon they were deeply integrated into the small‐scale fisheries (SSF) sector, which was already highly organised. Thus, synergies have been established between cofradías— fisheries organisations with a key role and strong responsibilities in the management of fisheries resources‐ and FLAGs. This article studies the interdependencies between cofradías and FLAGs and how these relationships can influence the development of SSF and their communities. Galician FLAGs and cofradías have created in general positive relationships that contributed to the development of the SSF sector. On the other hand, bureaucratic obligations are concentrated funding in larger cofradías , while the needs of the most vulnerable fishing stakeholders are less covered.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".